Journal
JOURNAL OF GEOPHYSICAL RESEARCH-SPACE PHYSICS
Volume 121, Issue 5, Pages 4611-4625Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1002/2015JA022132
Keywords
Van Allen Probes; electron number density; neural networks
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Funding
- NASA [NNX10AK99G, NNX13AE34G, NNX14AC04G, NNX15AI94G]
- NSF [AGS-1243183]
- UC Lab Fee award [116720]
- Horizon award [637302]
- International Space Science Institute (ISSI)
- JHU/APL under NASA [921647, NAS5-01072]
- NASA [807557, NNX15AI94G, 686453, NNX14AC04G] Funding Source: Federal RePORTER
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We present the Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm for automatic inference of the electron number density from plasma wave measurements made on board NASA's Van Allen Probes mission. A feedforward neural network is developed to determine the upper hybrid resonance frequency, f(uhr), from electric field measurements, which is then used to calculate the electron number density. In previous missions, the plasma resonance bands were manually identified, and there have been few attempts to do robust, routine automated detections. We describe the design and implementation of the algorithm and perform an initial analysis of the resulting electron number density distribution obtained by applying NURD to 2.5 years of data collected with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite of the Van Allen Probes mission. Densities obtained by NURD are compared to those obtained by another recently developed automated technique and also to an existing empirical plasmasphere and trough density model.
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